A computed tomography (CT) scanner includes an x-ray tube that emits radiation that traverses an examination region and a portion of an object or subject therein. A detector detects radiation traversing the examination region and generates projection data indicative of the detected radiation. A reconstructor reconstructs the projection data and generates volumetric of image data indicative of the portion of the object or subject in the examination region. The image data is voluminous, and sub-volumes of the image data typically are sequentially visualized via a display through a series of two-dimensional (2D) slices in an axial, sagittal, coronal and/or oblique plane making up the image data. Generally, the user scrolls through the sub-volumes, selecting and/or changing the slice plane and/or the slice thickness, and utilizes various standard visualization tools such as zoom, rotate, pan, etc.
Unfortunately, while scrolling through the large volume of image data, small nodules, such as lymph nodes or tumors, or vessels can be easily overlooked and/or visually occluded by other structure, and the evaluation of these structures may be essential for oncologic diagnosis, staging and therapy monitoring, as well as other medical applications. Nodules and vessels can be explicitly identified and segmented via state of the art computer aided detection approaches, with discrete labeling effectively applied to each voxel of the input image volume. Unfortunately, such labeling will necessarily have a certain error rate, and the inherent uncertainty generally will not be visible to the clinician evaluating the image data. As such, regulatory approval for this kind of computer aided detection and segmentation may be difficult and costly to achieve. Furthermore, a change to the original input image data volume by applying graphical markers may be less than desirable since visual perception of the clinician is trained on original image data.